Contrastive explanations

Human explanations are often contrastive, meaning that they do not answer the indeterminate “Why?” question, but instead “Why P, rather than Q?”. For example, when a mortgage application is denied, we are not interested in a very long list of tiny little details that all contributed to that decision, but we want a to-the-point explanation that shows us what we minimally have to change to get the mortgage.

For example, the CEM method supports such an explanation by finding the minimal set of features that lead to prediction P (so this looks like an anchor explanation), and additionally a minimal set of features that should be absent to maintain decision P instead of the closest class Q (which is somewhat similar to a counterfactual ).

AI Explainability 360

The AI Explainability 360 (AIX360) toolkit is a Python library that offers a wide range of explanation types as well as some explainability metrics. AIX360 offers excellent guidance material, an interactive demo as well as developer tutorials. What’s particularly good about this material is that it stimulates reflection on which type of explanation is appropriate, not only from a technical point of view, but also with respect to the target explainer and explainee. Read more...

Alibi

Alibi is an open-source Python library that supports various interpretability techniques and a broad array of explanation types. The README already provides an overview of the supported methods and when they are applicable. The following table with supported methods is copied from the README (slightly abbreviated): Supported methods Method Models Explanations Classification Regression Tabular Text Images Categorical features ALE BB global ✔ ✔ ✔ Anchors BB local ✔ ✔ ✔ ✔ ✔ CEM BB* TF/Keras local ✔ ✔ ✔ Counterfactuals BB* TF/Keras local ✔ ✔ ✔ Prototype Counterfactuals BB* TF/Keras local ✔ ✔ ✔ ✔ Integrated Gradients TF/Keras local ✔ ✔ ✔ ✔ ✔ ✔ Kernel SHAP BB local global ✔ ✔ ✔ ✔ Tree SHAP WB local global ✔ ✔ ✔ ✔ The README also explains the keys: Read more...

Contrastive Explanation Method (CEM)

Dhurandhar et al. support a type of contrastive explanation based on what they call pertinent negatives. A contrastive explanation answers the question: “Why P, rather than Q”? CEM supports such an explanation by finding the minimal set of features that lead to prediction P (a pertinent positive that resembles an anchor explanation), and additionally a minimal set of features that should be absent to maintain decision P instead of the decision for closest class Q (a pertinent negative that is somewhat similar to a counterfactual ). Read more...